Bayesian integration of visual and vestibular signals for heading

Abstract

Self-motion through an environment involves a composite of signals such as visual and vestibular cues. Building upon previous results showing that visual and vestibular signals combine in a statistically optimal fashion, we investigated the relative weights of visual and vestibular cues during self-motion. This experiment was comprised of three experimental conditions: vestibular alone, visual alone (with four different standard heading values), and visual-vestibular combined. In the combined cue condition, inter-sensory conflicts were introduced (Δ = ±6° or ±10°). Participants performed a 2-interval forced choice task in all conditions and were asked to judge in which of the two intervals they moved more to the right. The cue-conflict condition revealed the relative weights associated with each modality. We found that even when there was a relatively large conflict between the visual and vestibular cues, participants exhibited a statistically optimal reduction in variance. On the other hand, we found that the pattern of results in the unimodal conditions did not predict the weights in the combined cue condition. Specifically, visual-vestibular cue combination was not predicted solely by the reliability of each cue, but rather more weight was given to the vestibular cue.

How Optic Flow and Inertial Cues Improve Motion Perception

“…Indeed, empirically measured heading discrimination thresholds during combined vestibular -visual heading perception were close to the predictions produced by a statistically optimal cue combination rule (Gu et al 2008;Fetsch et al 2009Fetsch et al , 2011Fetsch et al , 2013Butler et al 2010Butler et al , 2015de Winkel et al 2013). …”

“…Psychophysical studies of heading discrimination using two-alternative forcedchoice tasks have reported vestibular heading discrimination thresholds in darkness that are as small as a few degrees Fetsch et al 2009;Butler et al 2010Butler et al , 2015de Winkel et al 2010;Drugowitsch et al 2014). Such threshold values are comparable (although larger) with those described in visual heading discrimi-nation tasks (Warren and Hannon 1990;Royden et al 1992;van den Berg and Brenner 1994;Stone and Perrone 1997).…”

“…In particular, as illustrated by the psychometric functions in Figure 2C, the precision of perception in a heading discrimination task (Fig. 2B) increases when both vestibular (i.e., inertial) and visual (i.e., optic flow) signals indicate self-motion as compared with when either cue is present alone (Gu et al 2008;Fetsch et al 2009Fetsch et al , 2011Fetsch et al , 2013Butler et al 2010Butler et al , 2015de Winkel et al 2013). Precision is the sensitivity or threshold (also called "reliability," computed as the "sigma" of the cumulative Gaussian fit of a psychometric function) (C, vertical dashed lines).…”

Iterative Bayesian Estimation as an Explanation for Range and Regression Effects: A Study on Human Path Integration

“…In line with this work, the variability in angular displacement perception has been proposed to be a result of Bayesian fusion of sensory inputs (Butler et al, 2010) and cognitive movement velocity (Jürgens and Becker, 2006). In the present work, however, movement velocity was varied randomly and independently of the experience condition.…”

Integration of vestibular and proprioceptive signals for spatial updating

“…Research on cue integration has provided support for the idea that, for a number of different sensory systems (e.g., visual-auditory, visual-haptic), cues are often combined in a ''statistically optimal'' manner (e.g., Alais and Burr 2004;Bülthoff and Yuille 1996;Cheng et al 2007;Ernst and Banks 2002;Ernst and Bülthoff 2004;Knill and Saunders 2003;Körding and Wolpert 2004;MacNeilage et al 2007). In this context, optimality refers to a cue combination, which results in the most reliable estimate possible given the available sensory input.…”